data science in La X

As the [catholic] daily La X has a special “Sciences&éthique” report on data science and scientists, my mom [a long time subscriber] mailed me [by post] the central pages where it appeared. The contents are not great, focusing as often on a few sentences from  and missing on the fundamental limitations of self-learning algorithms. As an aside, the leaflet contained a short interview by Jean-Stéphane Dhersin, who is head of the CNRS ModCov19 centralising platform [and anecdotally a neighbour] on the notion that a predictive model in epidemiology can be both scientific and imprecise.

This is the worst of times

the limits of R

It has been repeated many times on many platforms, the R (or R⁰) number is not a great summary about the COVID-19 pandemic, see eg Rossman’s warning in The Conversation, but Nature chose to stress it one more time (in its 16 Jul edition). Or twice when considering a similar piece in Nature Physics. As Boris Johnson made it a central tool of his governmental communication policy. And some mayors started asking for their own local R numbers! It is obviously tempting to turn the messy and complex reality of this planetary crisis into a single number and even a single indicator R<1, but it is unhelpful and worse, from the epidemiology models being wrong (or at least oversimplifying) to the data being wrong (i.e., incomplete, biased and late), to the predictions being wrong (except for predicting the past). Nothing outrageous from the said Nature article, pointing out diverse degrees of uncertainty and variability and stressing the need to immediately address clusters rather than using the dummy R. As an aside, the repeated use of nowcasting instead of forecasting sounds like a perfect journalist fad, given that it does not seem to be based on a different model of infection or on a different statistical technique. (There is a nowcasting package in R, though!) And a wee bit later I have been pointed out at an extended discussion of an R estimation paper on Radford Neal’s blog.

Bayes @ NYT

A tribune in the NYT of yesterday on the importance of being Bayesian. When an epidemiologist. Tribune that was forwarded to me by a few friends (and which I missed on my addictive monitoring of the journal!). It is written by Siobhan Roberts, a Canadian journalist writing about mathematics (and obviously statistics). And it brings to the general public the main motivation for adopting a Bayesian approach, namely its coherent handling of uncertainty and its ability to update in the face of new information. (Although it might be noted that other flavours of statistical analysis are also able to update their conclusions when given more data.) The COVID situation is a perfect case study in Bayesianism, in that there are so many levels of uncertainty and imprecision, from the models themselves, to the data, to the outcome of the tests, &tc. The article is journalisty, of course, but it quotes from a range of statisticians and epidemiologists, including Susan Holmes, whom I learned was quarantined 105 days in rural Portugal!, developing a hierarchical Bayes modelling of the prevalent  SEIR model, and David Spiegelhalter, discussing Cromwell’s Law (or better, humility law, for avoiding the reference to a fanatic and tyrannic Puritan who put Ireland to fire and the sword!, and had in fact very little humility for himself). Reading the comments is both hilarious (it does not take long to reach the point when Trump is mentioned, and Taleb’s stance on models and tails makes an appearance) and revealing, as many readers do not understand the meaning of Bayes’ inversion between causes and effects, or even the meaning of Jeffreys’ bar, |, as conditioning.

preprints promote confusion and distorsion, and don’t blame journalists!

“…anyone considering publicizing a preprint have a responsibility.”

On my way to the airport, flying to B’ham, I read an older issue of Nature that contained this incredible editorial entry from Tom Sheldon Tim Horton, calling for regulation of preprints or worse, for the reason that journalists could misunderstand their contents and over-hype a minor or worse wrong claim. Taking as mistaken illustration the case of the Séralini et al. paper, about the Monsanto maize, which happened to be published under “embargo” conditions and reproduced in most media before a scientific storm erupted on the lack of significance of the samples. This call is unbelievably cheeky and downright absurd as it shifts the responsibility away from the journalists to the scientific community, throwing the “check your sources” principle of investigative journalism down the drain. As if the only reason for immediately publishing front-page discoveries is not to beat the competition and attract more readers…

The irony of seeing this piece in Nature is that a few pages later, there is a news entry on German and Swedish institutions breaking negotiations with Elsevier, as the publisher refuses to join a global package of open source publications. Nothing seems amiss about this nice aspect of scientific publishing with the author of this editorial, nor with the further reports of retraction of published paper in the same issue. Presumably because journalists have already moved to the next hot discovery by the time the retractions at last appear…! And to answer the final question of “Should all preprints be emblazoned with a warning aimed at journalists that work has not been peer reviewed?”, no, no, and no: preprints are not written for journalists or the general public. Unsurprisingly, the tribune induced outraged reactions from Nature readers.